High-throughput transcriptome sequencing

Transcription

1 High-throughput transcriptome sequencing Erik Kristiansson Department of Zoology Department of Neuroscience and Physiology University of Gothenburg, Sweden

2 Outline Genome sequencing High-throughput sequencing techniques Overview Massively parallel pyrosequencing Transcriptome sequencing Why? How? Example: The sequencing of the eelpout transcriptome Gene expression measurement using highthroughput sequencing

3 Genome sequencing Haemophilus influenzae, 1995 First sequenced free living organism 1800 genes, 1.8 million base pairs Saccharomyces cerevisiae, 1997 First sequenced eukaryote Genome consists of 6000 genes and 12 million base pairs 7 years of sequencing Homo sapiens, 2003 Genome consists of genes and 3.25 billion base pairs 13 years of sequencing

4 Genome sequencing Planned whole-genome sequencing projects for vertebrates Vertebrate species distribution

5 Genome sequencing Today more than 1000 species have been sequenced. However, the choice of species is biased. Many important taxonomic groups still lacks species with sequenced genome! Whole-genome sequencing is expensive. A coverage of ~10 times is needed for a reliable build. Polyploidy makes genome sequencing complicated. Species with more than two sets of chromosomes are very hard to sequence (e.g. zebrafish).

6 High-throughput sequencing Second generation sequencing technology Currently three major techniques on the market. Technology Company Throughput Cost/base Read length Parallel pyrosequencing 454 Life Sciences /Roche 20 million bases per hour 0.01$ bases Solexa Illumina 80 million bases per hour 0.005$ 35 bases SOLiD Applied Biosystems 100 million bases per hour 0.002$ 35 bases Traditional sequencing 0.05 million bases per hour 0.5$ up to 1000 bases

7 High-throughput sequencing Picture taken from Rothberg & Leamon, Nature Biotechnology 2008

8 Massively parallel pyrosequencing Advantages to Sanger sequencing High throughput More accurate less than 1% error rate (Huse et al. 2007) More sensitive high depth Disadvantages Limited sequence length ( bases) 454 Life Science pyrosequencer Year Technique Performance (bp/day) 2004 Traditional sequencing (Sanger) 1 million 2005 Parallel pyrosequencing GS20 70 million 2007 Parallel pyrosequencing GS FLX 200 million 2008 Parallel pyrosequencing GS FLX Titanium 500 million

9 Massively parallel pyrosequencing

10 Massively parallel pyrosequencing Nucleotides are flowed sequentially (a) A signal is generated for each nucleotide incorporation (b) A CCD camera is generating an image after each flow (c) The signal strength is proportional to the number of incorporated nucleotides.

11 Massively parallel pyrosequencing

12 Massively parallel pyrosequencing

13 Transcriptome sequencing Genome sequencing is expensive, even with high-throughput sequencing technology. The cost for a higher eukaryote is ~$10,000,000. In transcriptome sequencing only the transcribed protein coding parts of genome is sequenced.

14 Transcriptome sequencing More than 50% of the genome is estimated to be transcribed (some form). ~ 10% is estimated to be functional But only ~1.5% (!!!) of the human genome is protein coding. Whole-genome sequencing generates a lot of data that is not of primary interest.

15 Transcriptome sequencing To measure gene expression we need to know the sequence of the genes. Microarray probes PCR primers Only the transcriptome need. The complete genome not necessary. However, traditional transcriptome sequencing (ESTs) Expensive Error prone Not deep enough There exists ~60 million ESTs for eukaryotes. 27 million of these are for vertebrates.

16 De novo transcriptome sequencing using massively parallel pyrosequencing Genome Transcripts ~ 3000 bp Transcriptome Massively parallel pyrosequencing ~ 250 bp

17 How much data do we get? The result from one run on a Genome Sequencer FLX reads 400 bases bases The transcriptome of a higher eukaryote is up to 50 million bases. We can, theoretically, cover this transcriptome 5 times. However, the limited read length will have an negative effect!

18 How much data do we get? Gene Reads from sequencing How many data do we need to remove all gaps?

19 How much data do we get?

20 Sequence data processing >E6LSDQW02HPHGI TGACTAAGATCCATCACATCAGGCCAGGTAGGAGTCTCTTATATTAGGTATCAATACCTTCCGGGT GGATACCTTTGAGGCATAAGCTGGACAGGCACAGAACCTCGAGGCAGAACTTCCCGACTGCTTGAT GTGTATCAAGGTCAATCAATCTGAAAATCAGCTGCCTAAGCACCAGTTCAAAAAAAAAAAAGAATA TTTGCTCAACTCCTCTTAGTAGCTGAGCGGGCTGGCAAGGC >E5R7OVD09FMUGM TGACTAACTGTAGACACACAACACATCAACACACACACACACACACACACACACACACACACACAC TAGANACACACACACACACACACACACACACACACACACACACACTACTATAATAAATAAAGAAGA AGAAGTAGTTAGTTAGTACTTAACGTTAACGGTACGGTACGTAGGTACGGTAACCGGTAACCGGTA ACCCGGAACCGTACGGTACGGTCGGTACGGTACGGTACGGTACGTACGTAACCGTTAAAAACCGGT TTAAAAAGGTAAAAAGGGTAAAAAGGGTTAAAACGGGTTAACGGGTAACGTAGTAGNA >E6LSDQW02GGDBU TGACTAACAAATTTTAATTACACTTAAGGTGTATATTTTCTATGCAACCCATCAATTCAAGAGGTG TAATGTGCTGATGACTATTTGTAATCGTTATACATTCTGACCCGAAGTCAGAAAGTATTTCTCTGT CTGTGTGTTCACAGGCAGTGTGGTTGATTACATGAAATTCAGTACATTTGCAGTCTCGTTGCCCTT CTCACCTGCCTTTCGTCATTACCGACGGTATTGAATTTCGTTTTCCCCGTTGGGGTTCTCCGGACA AGGAG

21 Sequences producing significant alignments: (bits) Value Ecoligenome 519 e-148 >Ecoligenome Length = Score = 519 bits (262), Expect = e-148 Identities = 262/262 (100%) Strand = Plus / Minus Query: 8 caaattttaattacacttaaggtgtatattttctatgcaacccatcaattcaagaggtgt 67 Sbjct: caaattttaattacacttaaggtgtatattttctatgcaacccatcaattcaagaggtgt Query: 68 aatgtgctgatgactatttgtaatcgttatacattctgacccgaagtcagaaagtatttc 127 Sbjct: aatgtgctgatgactatttgtaatcgttatacattctgacccgaagtcagaaagtatttc Query: 128 tctgtctgtgtgttcacaggcagtgtggttgattacatgaaattcagtacatttgcagtc 187 Sbjct: tctgtctgtgtgttcacaggcagtgtggttgattacatgaaattcagtacatttgcagtc Query: 188 tcgttgcccttctcacctgcctttcgtcattaccgacggtattgaatttcgttttccccg 247 Sbjct: tcgttgcccttctcacctgcctttcgtcattaccgacggtattgaatttcgttttccccg Query: 248 ttggggttctccggacaaggag 269 Sbjct: ttggggttctccggacaaggag

22 Sequence cleaning Removal of undesirable sequences which may disturb sequence assembly Better safe than sorry low complexity regions contains very little information Tags from 454 sequencing A tag TGACTAA B tag TTAGTAG The tags removed by pattern matching

23 Contamination mrna from other types of species rrna or other unwanted types of RNA Repetitive elements polya-tails Sequence cleaning Simple Sequence Repeats (SSR) More complex repeats like SINEs, LINEs and transposons Repetitive elements are typically contained in the untranslated regions (UTRs)

24 Sequence cleaning RepeatMasker is a tool for identification of repetitive elements ab initio prediction of repeats database matching Repbase Update is a database with Transposable elements Simple Sequence Repeats Pseudogenes

25 Assembly True transcript Reads from sequencing Assembled sequences Contigs and singlets Similarity threshold Less strict setting results in longer contigs with more errors More strict setting results in shorter contigs with fewer errors

26 The Gene Indices Clustering Tools Reads Clusters blastclust CAP3 Contigs

27 Functional similarity from sequence similarity Assign information to the assembled transcripts Gene description Annotation Functional annotation (e.g. Gene Ontology, pathways, etc.) Homology and interactions GenBank UniProt ensembl

28 Direction of transcription Massively parallel pyrosequencing ignores the direction of transcription Correct direction of transcription is however crucial for measuring gene expression AATTTTTCGATCTCCCTGCAAGACGGCTCATTT 5 3 I I G S V S V S E G L AATATAACATCACCTGCAAATTTTTCGATCTCCCTGCAAGACGGCTCATTTGGCTCATAAC TTATATTGTAGTGGACGTTTAAAAAGCTAGAGGGACGTTCTGCCGAGTAAACCGAGTATTG 3 5

29 BLAST test both strands and reports the best >O42430 CP1A1_LIMLI Cytochrome P450 1A1 - Limanda limanda (Dab) Length = 521 Minus Strand HSPs: Score = 2347 (908.7 bits), Expect = 1.7e-266, P = 1.7e-266 Identities = 440/520 (84%), Positives = 478/520 (91%), Frame = -3 Query: 1593 MVLTILPFIGPVSVSESLVAMTTLCLVYLIFKFFHTDIXXXXXXXXXXXXXXXXXNVLEV 1414 M+L +LPFIG VSVSESLVAMTT+CLVYLI KFF T+I NVLE+ Sbjct: 1 MMLMMLPFIGSVSVSESLVAMTTVCLVYLILKFFQTEIPEGLRRLPGPKPLPIIGNVLEM 60 Query: 1413 GSRPYLSLTAMSKRYGNIFQIQIGMRPVVVLSGSDTLRQALIKQGDDFAGRPDLYSFRLI 1234 GSRPYLSLTAMSKRYGN+FQIQIGMRPVVVLSGS+T+RQALIKQGDDFAGRPDLYSFR I Sbjct: 61 GSRPYLSLTAMSKRYGNVFQIQIGMRPVVVLSGSETVRQALIKQGDDFAGRPDLYSFRFI 120 FrameFinder from the estate software suite ab initio prediction of protein coding regions Returns location for protein coding regions and the predicted protein

30 Case study: Sequencing of the transcriptome of Zoarces viviparus The BALCOFISH project No suitable model species Zoarces viviparus (eelpout) Stationary Gives birth to live young Large-scale gene expression assays in eelpout Sequencing of the liver transcriptome Design of an eelpout microarray

31 The analysis pipeline 1. Pre-processing Removes: Redudant 454-reads (ghost reads) 5 /3 sequencing vectors 2. SeqClean Removes: PolyA-tails Simple repeats Bacterial contamination 4. Assembly Contig assembly using megablast and CAP3. 3. RepeatMasker Masks/removes: Low-complexity regions (Transposons, etc.) Contamination (rrna, etc)

32 Assembly results and statistics Massively parallel pyrosequencing on a GS FLX reads with an average length of 237 bases 90 million bases in total Contigs Singlets Total Number of sequences 36,110 17,347 53,457 Number of bases 14,250,156 4,050,061 18,300,217 Average length Average coverage Annotated 89.2% 87.3% 88.6%

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36 Assembly results and statistics The 18 million bases covers ~40% of the total eelpout transcriptome Matches ~8,000 genes in stickleback Few stickleback genes are represented only by eelpout singlets.

37 Pyrosequencing Genbank Gene Accession Length Accession Length Vitellogenin ZOVI ,826 AJ ,229 Zona Pelucida 2 ZOVI , Zona Pelucida 3 ZOVI Estrogen receptor ZOVI AY ,256 Metallothionein ZOVI X Heat-shock protein 70 ZOVI , Heat-shock protein 90 ZOVI Cytochrome P450 1A ZOVI , Superoxide dismutase ZOVI Glutathione peroxidase ZOVI ,

38 Novel genes in the eelpout data? Previous studies report ~10% transcripts from regions without annotation (e.g. A. thaliana, C. elegans) Alignment against five fish genomes 19,000 transcripts aligned in three out of five 4% of these aligned outside annotated regions 717 base pairs

39 Randomized regions Transcripts inside annotated regions Transcripts outside annotated regions

40 Measuring gene expression using high-throughput sequencing High-throughput sequencing can be used to measure gene expression for species with known genomes 1. Sequence the transcriptome 2. Count the number of times each gene appears Advantages Low technical noise No cross-hybridization Disadvantages Many reads are needed to measure low expressed genes Expensive Fully sequenced genome more important

41 Measuring gene expression using high-throughput sequencing The correlation between high-throughput sequencing and microarrays is between 50-80% Data for all sequencing techniques is still missing. t Hoen et al Correlation is ~60% t Hoen et al Correlation is ~70%

42 Gene expression measurement with highthroughput sequencing Correlation for annotated transcripts was ~60% Kristiansson et al Characterization of the Zoarces viviparus transcriptome using massively parallel pryosequencing.

43 Gene expression measurement with highthroughput sequencing High-throughput sequencing can be used to measure gene expression for species with known genomes 1. Sequence the transcriptome 2. Count the number of times each gene appears Advantages Low technical noise No cross-hybridization Disadvantages Many reads are needed to measure low expressed genes Expensive Fully sequenced genome more important

44 Gene expression measurement with highthroughput sequencing The correlation between high-throughput sequencing and microarrays is between 50-80% Data for all sequencing techniques is still missing. t Hoen et al Correlation is ~60% t Hoen et al Correlation is ~70%

45 Gene expression measurement with high-throughput sequencing Correlation for annotated transcripts was ~60% Kristiansson et al Characterization of the Zoarces viviparus transcriptome using massively parallel pryosequencing.

46 Summary The second generation sequencing techniques can generate vasts amount of sequence data. Illumina and SOLiD sequencing can generate more data than massively parallel pyrosequencing but with shorter reads. Massively parallel pyrosequencing can be used for de novo transcriptome sequencing. One run is enough to assemble large parts of the transcriptome of a higher eukaryote. Gene expression measurements using highthroughput sequencing has both advantages and disadvantages compared to microarrays. The correlation is around 60-70%.